{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年2月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201906</td>\n",
       "      <td>12990</td>\n",
       "      <td>12000</td>\n",
       "      <td>98669</td>\n",
       "      <td>323172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>201907</td>\n",
       "      <td>13209</td>\n",
       "      <td>12000</td>\n",
       "      <td>94918</td>\n",
       "      <td>308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>201908</td>\n",
       "      <td>13388</td>\n",
       "      <td>12500</td>\n",
       "      <td>94569</td>\n",
       "      <td>303830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>201909</td>\n",
       "      <td>13421</td>\n",
       "      <td>12500</td>\n",
       "      <td>90804</td>\n",
       "      <td>292998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>201910</td>\n",
       "      <td>13337</td>\n",
       "      <td>12500</td>\n",
       "      <td>87620</td>\n",
       "      <td>284579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>201911</td>\n",
       "      <td>13451</td>\n",
       "      <td>12500</td>\n",
       "      <td>84685</td>\n",
       "      <td>277672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>201912</td>\n",
       "      <td>13554</td>\n",
       "      <td>12500</td>\n",
       "      <td>84141</td>\n",
       "      <td>276906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>202001</td>\n",
       "      <td>13632</td>\n",
       "      <td>12500</td>\n",
       "      <td>78009</td>\n",
       "      <td>258456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>202002</td>\n",
       "      <td>13716</td>\n",
       "      <td>12500</td>\n",
       "      <td>71577</td>\n",
       "      <td>238465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0  201906        12990          12000     98669     323172\n",
       "1  201907        13209          12000     94918     308802\n",
       "2  201908        13388          12500     94569     303830\n",
       "3  201909        13421          12500     90804     292998\n",
       "4  201910        13337          12500     87620     284579\n",
       "5  201911        13451          12500     84685     277672\n",
       "6  201912        13554          12500     84141     276906\n",
       "7  202001        13632          12500     78009     258456\n",
       "8  202002        13716          12500     71577     238465"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"Month\",\"JD_Count\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照职能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "区块链工程师 平均工资为20543\n"
     ]
    }
   ],
   "source": [
    "sql=f'select AVG(monthly_salary)  from _{year}{month:02} where expert_blockchain=1 and monthly_salary>0'\n",
    "salary_blockchain=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'区块链工程师 平均工资为{salary_blockchain}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "算法工程师 平均工资为19321\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and career='算法工程师'\"\n",
    "salary_algorithm=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'算法工程师 平均工资为{salary_algorithm}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系统架构师 平均工资为23021\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and career='系统架构师'\"\n",
    "salary_architect=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'系统架构师 平均工资为{salary_architect}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Java工程师 平均工资为14178\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and pl_java=1\"\n",
    "salary_java=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Java工程师 平均工资为{salary_java}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python工程师 平均工资为18104\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and pl_python=1\"\n",
    "salary_python=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Python工程师 平均工资为{salary_python}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func['职能']=['区块链工程师', '算法工程师','系统架构师', 'Java工程师',  'Python工程师']\n",
    "data_func['工资']=[salary_blockchain, salary_algorithm, salary_architect, salary_java, salary_python]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职能</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>系统架构师</td>\n",
       "      <td>23021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>区块链工程师</td>\n",
       "      <td>20543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>19321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Python工程师</td>\n",
       "      <td>18104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Java工程师</td>\n",
       "      <td>14178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          职能     工资\n",
       "2      系统架构师  23021\n",
       "0     区块链工程师  20543\n",
       "1      算法工程师  19321\n",
       "4  Python工程师  18104\n",
       "3    Java工程师  14178"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_func.sort_values(by='工资', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
